HaSAPPy

HaSAPPy analyzes next-generation sequencing (NGS) datasets from pooled haploid mammalian cell screens to detect and characterize insertional mutations and predict candidate genes from enrichment patterns.

Key Features:

• Insertion Location Identification: Pinpoints exact insertion locations genome-wide from NGS data.
• Gene-Level Mapping: Maps identified insertions to specific genes to provide gene-context for mutations.
• Classification of Insertion Effects: Classifies insertions based on their effects on gene function.
• Candidate Gene Prediction: Identifies candidate genes by detecting enrichment patterns of insertional mutations after selection while evaluating multiple parameters simultaneously.
• Benchmarking and Validation: Performance has been benchmarked using datasets from genetic screens, including human haploid cell screens with validated candidates.

Scientific Applications:

• Insertional mutagenesis screens in haploid cells: Analysis of pooled haploid mammalian cell screens to discover genes affecting selected phenotypes.
• Identification of X chromosome inactivation factors: Screening for silencing factors of X chromosome inactivation in haploid mouse embryonic stem cells.
• Discovery of pathway components: Detection of candidate genes from enrichment patterns to uncover components of complex genetic pathways and mechanisms.

Methodology:

Pinpoints insertions genome-wide, maps insertions to genes, classifies insertional effects, and integrates multiple parameters to identify enriched insertional mutations following selection.